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Evidence record 6890 · automatically gathered

DeepXplain: XAI-Guided Autonomous Defense Against Multi-Stage APT Campaigns

Advanced Persistent Threats (APTs) are stealthy, multi-stage attacks that require adaptive and timely defense. While deep reinforcement learning (DRL) enables autonomous cyber defense, its decisions are often opaque and difficult to trust in operational environments. This paper presents DeepXplain, an explainable DRL framework for stage-aware APT defense. Building on our prior DeepStage model, DeepXplain integrates provenance-based graph learning, temporal stage estimation, and a unified XAI pip

Record details

Published: 22 March 2026
Source: arXiv
Category: Research
Topics: Military & security · Transparency · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Military & security · Shared topic: Environment

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arXiv · 17 March 2026

Shared topic: Transparency · Shared topic: Environment

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Shared topic: Transparency · Shared topic: Environment

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Shared topic: Military & security · Shared topic: Environment

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Shared topic: Transparency · Shared topic: Environment

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arXiv · 12 March 2026

Shared topic: Transparency · Shared topic: Environment

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How to cite this record

ethics.ai (22 March 2026), “DeepXplain: XAI-Guided Autonomous Defense Against Multi-Stage APT Campaigns,” evidence record 6890, https://ethics.ai/record/6890 (originally published by arXiv).

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